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The improvement in numerical weather prediction in the last three decades is due to improvements in atmospheric models, observations and data assimilation (the science of combining forecasts and observations to create model initial conditions). In recent years, the Ensemble Kalman filter has become the most advanced approach for performing data assimilation. I will introduce data assimilation, Ensemble Kalman filters and new advances that extend their usefulness. In addition, I will show application of these algorithms to real and simulated observation examples, illustrating the potential of these new approaches. The results include seven years of global ocean data assimilation, estimation of surface carbon, heat and moisture fluxes from atmospheric data assimilation, and a comparison of four-dimensional variational data assimilation (4D-Var) and an Ensemble Kalman filter for a simple “coupled ocean-atmosphere model”.

Bio

Eugenia Kalnay is a Distinguished University Professor in the Department of Atmospheric and Oceanic Science at the University of Maryland. For ten years she served as Director of the Environmental Modeling Center within the National Weather Service, which is a pioneer in both the fundamental science and practical applications of numerical weather prediction. In 2009, Dr. Kalnay won the prestigious World Meteorological Organization IMO Prize. Her work on the impact of land use on climate change was chosen by Discovery Magazine as one of the top 100 science results of the year, and her seminal paper on reanalysis is the most cited paper in geosciences. Dr. Kalnay was the first woman to obtain a doctorate from the MIT Department of Meteorology (1971) and the first female professor there. Her current research interests lie in data assimilation, numerical weather prediction, coupled ocean-atmosphere modeling and climate change.